NDLI: A Multi-Expert System to Classify Fluorescent Intensity in Antinuclear Autoantibodies Testing

Content Provider	IEEE Xplore Digital Library
Author	Soda, P. Iannello, G.
Copyright Year	2006
Description	Author affiliation: Universita Campus Bio-Medico di Roma, Rome (Soda, P.; Iannello, G.)
Abstract	Indirect immunofluorescence is the recommended method for antinuclear autoantibodies (ANA) detection. IIF diagnosis requires estimating fluorescent intensity and pattern description, but resources and adequately trained personnel are not always available for these tasks. In this respect, an evident medical demand is the development of computer aided diagnosis tools that can offer a support to physician decision. In this paper we propose a system to classify the fluorescent intensity: initially we discuss two classifiers based on artificial neural networks that can recognize intrinsically dubious samples and whose error tolerance can be flexibly set according to a given rule. Since such classifiers complement one other, we adopt a multiple expert system that aggregates the two experts. The final decision of the system results from the combination of the outputs of the single experts. Measured performance shows error rates less than 1%, which candidates the method to be used in daily medical practice
Starting Page	219
Ending Page	224
File Size	405232
Page Count	6
File Format	PDF
ISBN	0769525171
ISSN	10637125
DOI	10.1109/CBMS.2006.21
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fluorescence System testing Medical diagnostic imaging Immune system Personnel Physics computing Artificial neural networks Computer errors Medical expert systems Aggregates
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging Computer Science Applications

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